Dust Detection on Solar Panels: A Computer Vision Approach

A significant challenge for Photovoltaic (PV) power systems is the accumulation of dust on solar panels, particularly prevalent in desert areas.Dust accumulation on solar panels cause a high degradation in the output power and thus, solar panels should be monitored and cleaned continuously to keep their efficiency high.Automating the inspection of solar panels can serve as a viable alternative of human inspection due to the impact of labor expenses and human difficulties on decision-making on such an environment.In this work, we are proposing a computer vision approach that is capable of inspecting solar panels and determines its condition in terms of dust accumulation.The proposed approach aims to prove the capability of dust detection on distinct panels by means of visible light imaging and computer vision techniques.It deploys both gray level co-occurrence matrix (GLCM) textural features and local binary patterns (LBP) of solar panels' images in addition to support vector machine (SVM) to build a classification model for this purpose.The proposed approach has been tested on images of solar panels that suffer from moderate and heavy accumulation of desert sands and dusts.The experimental findings successfully illustrated the effectiveness of the proposed feature description and the overall dust detection approach of solar panels with an accuracy of 94.3%.